Due to recent technological advances, individuals and organizations may quickly and easily share, access, and disseminate high volumes of digital information. For many individuals and organizations, the ease with which information may be electronically disseminated is empowering. However, the ubiquity of high-speed Internet access, smart mobile devices, and portable storage devices may pose unique challenges for individuals and organizations concerned with preventing the loss and/or exposure of sensitive data. Individuals and organizations are therefore increasingly looking to data loss prevention (“DLP”) solutions to protect their sensitive data.
Conventional DLP systems typically attempt to protect sensitive data through the use of describing and fingerprinting technologies. Describing technologies typically involve identifying matches to keywords, expressions, patterns, or file types, and by performing other signature-based detection techniques. Fingerprinting technologies, on other hand, typically involve identifying exact matches to whole or partial files. While potentially effective in protecting much of an organization's sensitive data, fingerprinting and describing technologies may fail to accurately identify new items of sensitive data (i.e., items of sensitive data that have not been encountered before) and/or modifications to existing items of sensitive data. Because of this, existing DLP systems may be unable to adequately monitor and protect various types of unstructured sensitive data and intellectual property, such as product formulas, source code, and sales and marketing reports.
In an attempt to more accurately detect and protect unstructured sensitive data, at least one provider of DLP software has explored using machine-learning techniques to identify sensitive data that is similar to, but not exactly the same as, known examples of sensitive data.